Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations2747
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory300.6 KiB
Average record size in memory112.0 B

Variable types

Numeric8
Categorical6
DateTime1

Alerts

address_stability_missing is highly overall correlated with cust_income_log and 1 other fieldsHigh correlation
age is highly overall correlated with job_stability_missingHigh correlation
cust_income is highly overall correlated with cust_income_logHigh correlation
cust_income_log is highly overall correlated with address_stability_missing and 1 other fieldsHigh correlation
employment is highly overall correlated with address_stability_missing and 1 other fieldsHigh correlation
job_stability_missing is highly overall correlated with age and 2 other fieldsHigh correlation
job_stability_years is highly overall correlated with job_stability_missingHigh correlation
address_stability_missing is highly imbalanced (84.6%) Imbalance
cocunut is uniformly distributed Uniform
cocunut has unique values Unique
years_with_bank has 276 (10.0%) zeros Zeros
cust_income has 76 (2.8%) zeros Zeros
cust_income_log has 76 (2.8%) zeros Zeros

Reproduction

Analysis started2025-05-17 15:11:25.866646
Analysis finished2025-05-17 15:11:46.451654
Duration20.59 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

cocunut
Real number (ℝ)

Uniform  Unique 

Distinct2747
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81374
Minimum80001
Maximum82747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2025-05-17T17:11:46.694657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum80001
5-th percentile80138.3
Q180687.5
median81374
Q382060.5
95-th percentile82609.7
Maximum82747
Range2746
Interquartile range (IQR)1373

Descriptive statistics

Standard deviation793.13492
Coefficient of variation (CV)0.0097467854
Kurtosis-1.2
Mean81374
Median Absolute Deviation (MAD)687
Skewness0
Sum2.2353438 × 108
Variance629063
MonotonicityStrictly increasing
2025-05-17T17:11:47.036653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80001 1
 
< 0.1%
81835 1
 
< 0.1%
81827 1
 
< 0.1%
81828 1
 
< 0.1%
81829 1
 
< 0.1%
81830 1
 
< 0.1%
81831 1
 
< 0.1%
81832 1
 
< 0.1%
81833 1
 
< 0.1%
81834 1
 
< 0.1%
Other values (2737) 2737
99.6%
ValueCountFrequency (%)
80001 1
< 0.1%
80002 1
< 0.1%
80003 1
< 0.1%
80004 1
< 0.1%
80005 1
< 0.1%
80006 1
< 0.1%
80007 1
< 0.1%
80008 1
< 0.1%
80009 1
< 0.1%
80010 1
< 0.1%
ValueCountFrequency (%)
82747 1
< 0.1%
82746 1
< 0.1%
82745 1
< 0.1%
82744 1
< 0.1%
82743 1
< 0.1%
82742 1
< 0.1%
82741 1
< 0.1%
82740 1
< 0.1%
82739 1
< 0.1%
82738 1
< 0.1%

age
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.736804
Minimum21
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2025-05-17T17:11:47.392653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile27.3
Q138
median49
Q360
95-th percentile69
Maximum74
Range53
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.186774
Coefficient of variation (CV)0.27057117
Kurtosis-1.0782457
Mean48.736804
Median Absolute Deviation (MAD)11
Skewness-0.079222786
Sum133880
Variance173.89101
MonotonicityNot monotonic
2025-05-17T17:11:47.917655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 77
 
2.8%
65 75
 
2.7%
45 74
 
2.7%
67 72
 
2.6%
62 71
 
2.6%
48 71
 
2.6%
63 70
 
2.5%
44 69
 
2.5%
39 67
 
2.4%
66 66
 
2.4%
Other values (44) 2035
74.1%
ValueCountFrequency (%)
21 4
 
0.1%
22 15
 
0.5%
23 14
 
0.5%
24 25
0.9%
25 24
0.9%
26 30
1.1%
27 26
0.9%
28 31
1.1%
29 59
2.1%
30 44
1.6%
ValueCountFrequency (%)
74 6
 
0.2%
73 8
 
0.3%
72 12
 
0.4%
71 30
 
1.1%
70 32
1.2%
69 58
2.1%
68 51
1.9%
67 72
2.6%
66 66
2.4%
65 75
2.7%

years_with_bank
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9912632
Minimum0
Maximum15
Zeros276
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2025-05-17T17:11:48.266654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q311
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.5228154
Coefficient of variation (CV)0.64692393
Kurtosis-1.4164684
Mean6.9912632
Median Absolute Deviation (MAD)4
Skewness-0.15373129
Sum19205
Variance20.45586
MonotonicityNot monotonic
2025-05-17T17:11:48.505655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11 413
15.0%
0 276
10.0%
12 275
10.0%
10 234
8.5%
4 230
8.4%
2 197
7.2%
3 176
 
6.4%
9 172
 
6.3%
1 171
 
6.2%
13 128
 
4.7%
Other values (6) 475
17.3%
ValueCountFrequency (%)
0 276
10.0%
1 171
6.2%
2 197
7.2%
3 176
6.4%
4 230
8.4%
5 119
4.3%
6 86
 
3.1%
7 76
 
2.8%
8 99
 
3.6%
9 172
6.3%
ValueCountFrequency (%)
15 32
 
1.2%
14 63
 
2.3%
13 128
 
4.7%
12 275
10.0%
11 413
15.0%
10 234
8.5%
9 172
6.3%
8 99
 
3.6%
7 76
 
2.8%
6 86
 
3.1%

marital_status
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
M
1930 
S
478 
W
 
177
D
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2747
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowW
3rd rowM
4th rowD
5th rowM

Common Values

ValueCountFrequency (%)
M 1930
70.3%
S 478
 
17.4%
W 177
 
6.4%
D 162
 
5.9%

Length

2025-05-17T17:11:48.823655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:49.114657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 1930
70.3%
s 478
 
17.4%
w 177
 
6.4%
d 162
 
5.9%

Most occurring characters

ValueCountFrequency (%)
M 1930
70.3%
S 478
 
17.4%
W 177
 
6.4%
D 162
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1930
70.3%
S 478
 
17.4%
W 177
 
6.4%
D 162
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1930
70.3%
S 478
 
17.4%
W 177
 
6.4%
D 162
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1930
70.3%
S 478
 
17.4%
W 177
 
6.4%
D 162
 
5.9%

education
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
HGH
1872 
BCR
741 
OTH
 
110
MAS
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8241
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHGH
2nd rowOTH
3rd rowBCR
4th rowBCR
5th rowHGH

Common Values

ValueCountFrequency (%)
HGH 1872
68.1%
BCR 741
 
27.0%
OTH 110
 
4.0%
MAS 24
 
0.9%

Length

2025-05-17T17:11:49.621654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:51.533297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hgh 1872
68.1%
bcr 741
 
27.0%
oth 110
 
4.0%
mas 24
 
0.9%

Most occurring characters

ValueCountFrequency (%)
H 3854
46.8%
G 1872
22.7%
B 741
 
9.0%
C 741
 
9.0%
R 741
 
9.0%
O 110
 
1.3%
T 110
 
1.3%
M 24
 
0.3%
A 24
 
0.3%
S 24
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 3854
46.8%
G 1872
22.7%
B 741
 
9.0%
C 741
 
9.0%
R 741
 
9.0%
O 110
 
1.3%
T 110
 
1.3%
M 24
 
0.3%
A 24
 
0.3%
S 24
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 3854
46.8%
G 1872
22.7%
B 741
 
9.0%
C 741
 
9.0%
R 741
 
9.0%
O 110
 
1.3%
T 110
 
1.3%
M 24
 
0.3%
A 24
 
0.3%
S 24
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 3854
46.8%
G 1872
22.7%
B 741
 
9.0%
C 741
 
9.0%
R 741
 
9.0%
O 110
 
1.3%
T 110
 
1.3%
M 24
 
0.3%
A 24
 
0.3%
S 24
 
0.3%

employment
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
PVE
1186 
STE
742 
RET
699 
MISC
120 

Length

Max length4
Median length3
Mean length3.043684
Min length3

Characters and Unicode

Total characters8361
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPVE
2nd rowRET
3rd rowSTE
4th rowMISC
5th rowPVE

Common Values

ValueCountFrequency (%)
PVE 1186
43.2%
STE 742
27.0%
RET 699
25.4%
MISC 120
 
4.4%

Length

2025-05-17T17:11:51.796299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:52.013297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pve 1186
43.2%
ste 742
27.0%
ret 699
25.4%
misc 120
 
4.4%

Most occurring characters

ValueCountFrequency (%)
E 2627
31.4%
T 1441
17.2%
P 1186
14.2%
V 1186
14.2%
S 862
 
10.3%
R 699
 
8.4%
M 120
 
1.4%
I 120
 
1.4%
C 120
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2627
31.4%
T 1441
17.2%
P 1186
14.2%
V 1186
14.2%
S 862
 
10.3%
R 699
 
8.4%
M 120
 
1.4%
I 120
 
1.4%
C 120
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2627
31.4%
T 1441
17.2%
P 1186
14.2%
V 1186
14.2%
S 862
 
10.3%
R 699
 
8.4%
M 120
 
1.4%
I 120
 
1.4%
C 120
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2627
31.4%
T 1441
17.2%
P 1186
14.2%
V 1186
14.2%
S 862
 
10.3%
R 699
 
8.4%
M 120
 
1.4%
I 120
 
1.4%
C 120
 
1.4%

gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
M
1379 
F
1368 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2747
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 1379
50.2%
F 1368
49.8%

Length

2025-05-17T17:11:52.296298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:52.444663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 1379
50.2%
f 1368
49.8%

Most occurring characters

ValueCountFrequency (%)
M 1379
50.2%
F 1368
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1379
50.2%
F 1368
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1379
50.2%
F 1368
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1379
50.2%
F 1368
49.8%

cust_income
Real number (ℝ)

High correlation  Zeros 

Distinct2370
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368.59353
Minimum0
Maximum7978.9615
Zeros76
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2025-05-17T17:11:52.680665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162.25399
Q1214.62477
median292.30769
Q3410.40346
95-th percentile819.53154
Maximum7978.9615
Range7978.9615
Interquartile range (IQR)195.77869

Descriptive statistics

Standard deviation333.43406
Coefficient of variation (CV)0.90461181
Kurtosis159.39583
Mean368.59353
Median Absolute Deviation (MAD)92.307692
Skewness8.9832876
Sum1012526.4
Variance111178.27
MonotonicityNot monotonic
2025-05-17T17:11:53.053661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
2.8%
307.6923077 25
 
0.9%
230.7692308 24
 
0.9%
269.2307692 24
 
0.9%
384.6153846 22
 
0.8%
192.3076923 21
 
0.8%
215.3846154 12
 
0.4%
276.9230769 11
 
0.4%
184.6153846 11
 
0.4%
346.1538462 10
 
0.4%
Other values (2360) 2511
91.4%
ValueCountFrequency (%)
0 76
2.8%
1.153846154 1
 
< 0.1%
117.3846154 1
 
< 0.1%
123.1 1
 
< 0.1%
129.8023077 1
 
< 0.1%
132.2076923 1
 
< 0.1%
137.4062308 1
 
< 0.1%
138.6440769 1
 
< 0.1%
139.9797692 1
 
< 0.1%
140.6102308 1
 
< 0.1%
ValueCountFrequency (%)
7978.961538 1
< 0.1%
6864.915385 1
< 0.1%
3053.984615 1
< 0.1%
3007.018846 1
< 0.1%
2711.907692 1
< 0.1%
2700.203769 1
< 0.1%
2589.938462 1
< 0.1%
2587.807692 1
< 0.1%
2523.1 1
< 0.1%
2346.221462 1
< 0.1%
Distinct1752
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
Minimum2001-09-11 00:00:00
Maximum2016-10-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-17T17:11:53.424663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:53.818663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

current_balance_eur
Real number (ℝ)

Distinct1137
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3122.6323
Minimum0
Maximum18176.994
Zeros18
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2025-05-17T17:11:54.183662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile269.25315
Q11274.9794
median2307.6923
Q34615.3846
95-th percentile7692.3077
Maximum18176.994
Range18176.994
Interquartile range (IQR)3340.4052

Descriptive statistics

Standard deviation2447.3621
Coefficient of variation (CV)0.78374968
Kurtosis3.1474657
Mean3122.6323
Median Absolute Deviation (MAD)1517.3077
Skewness1.3827727
Sum8577871
Variance5989581.1
MonotonicityNot monotonic
2025-05-17T17:11:54.524665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2307.692308 499
 
18.2%
4615.384615 207
 
7.5%
7692.307692 92
 
3.3%
2371.153846 37
 
1.3%
790.3846154 29
 
1.1%
5366.711538 21
 
0.8%
1580.769231 20
 
0.7%
0 18
 
0.7%
3951.923077 18
 
0.7%
1185.576923 17
 
0.6%
Other values (1127) 1789
65.1%
ValueCountFrequency (%)
0 18
0.7%
2.089923077 1
 
< 0.1%
18.14769231 1
 
< 0.1%
75.61676923 1
 
< 0.1%
78.43846154 1
 
< 0.1%
84.85615385 1
 
< 0.1%
92.16 1
 
< 0.1%
118.8461538 1
 
< 0.1%
118.8540769 1
 
< 0.1%
120.6153846 1
 
< 0.1%
ValueCountFrequency (%)
18176.99415 1
 
< 0.1%
18121.03669 1
 
< 0.1%
17966.65285 1
 
< 0.1%
16652.01969 1
 
< 0.1%
15775.59762 1
 
< 0.1%
15384.61538 1
 
< 0.1%
14923.75069 1
 
< 0.1%
14226.92308 1
 
< 0.1%
13846.15385 3
0.1%
13672.18462 1
 
< 0.1%

job_stability_years
Real number (ℝ)

High correlation 

Distinct1284
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.078648
Minimum-0.019178082
Maximum42.769863
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size21.6 KiB
2025-05-17T17:11:54.884661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.019178082
5-th percentile1.0136986
Q14.6794521
median8.7315068
Q311.912329
95-th percentile28.510685
Maximum42.769863
Range42.789041
Interquartile range (IQR)7.2328767

Descriptive statistics

Standard deviation7.9101843
Coefficient of variation (CV)0.78484576
Kurtosis2.304545
Mean10.078648
Median Absolute Deviation (MAD)3.7643836
Skewness1.5022342
Sum27686.047
Variance62.571016
MonotonicityNot monotonic
2025-05-17T17:11:55.215661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.731506849 711
 
25.9%
6.764383562 18
 
0.7%
0.6767123288 15
 
0.5%
0.2630136986 13
 
0.5%
0.5123287671 12
 
0.4%
2.676712329 12
 
0.4%
0.597260274 10
 
0.4%
10.10136986 10
 
0.4%
5.350684932 10
 
0.4%
1.01369863 10
 
0.4%
Other values (1274) 1926
70.1%
ValueCountFrequency (%)
-0.01917808219 1
 
< 0.1%
0.002739726027 1
 
< 0.1%
0.008219178082 1
 
< 0.1%
0.01095890411 1
 
< 0.1%
0.03835616438 1
 
< 0.1%
0.07397260274 1
 
< 0.1%
0.08767123288 1
 
< 0.1%
0.0904109589 1
 
< 0.1%
0.09315068493 4
 
0.1%
0.2630136986 13
0.5%
ValueCountFrequency (%)
42.76986301 1
< 0.1%
41.75342466 1
< 0.1%
41.25479452 1
< 0.1%
41.04109589 1
< 0.1%
40.79178082 1
< 0.1%
40.78082192 1
< 0.1%
39.98630137 1
< 0.1%
39.97808219 1
< 0.1%
39.78630137 1
< 0.1%
39.29041096 1
< 0.1%

address_stability_years
Real number (ℝ)

Distinct1600
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.520802
Minimum0.51232877
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2025-05-17T17:11:55.516661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.51232877
5-th percentile3.3619178
Q110.852055
median23.224658
Q335.49589
95-th percentile55.066301
Maximum60
Range59.487671
Interquartile range (IQR)24.643836

Descriptive statistics

Standard deviation15.633069
Coefficient of variation (CV)0.63754313
Kurtosis-0.62072625
Mean24.520802
Median Absolute Deviation (MAD)12.372603
Skewness0.4964408
Sum67358.644
Variance244.39285
MonotonicityNot monotonic
2025-05-17T17:11:55.851663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 77
 
2.8%
23.22465753 61
 
2.2%
16.77260274 41
 
1.5%
6.764383562 33
 
1.2%
36.78630137 31
 
1.1%
11.76712329 22
 
0.8%
8.767123288 22
 
0.8%
4.764383562 21
 
0.8%
9.767123288 21
 
0.8%
10.76712329 21
 
0.8%
Other values (1590) 2397
87.3%
ValueCountFrequency (%)
0.5123287671 1
 
< 0.1%
0.597260274 3
 
0.1%
0.6246575342 1
 
< 0.1%
0.6767123288 4
 
0.1%
0.7150684932 1
 
< 0.1%
0.7616438356 10
0.4%
0.9287671233 1
 
< 0.1%
1.01369863 1
 
< 0.1%
1.071232877 1
 
< 0.1%
1.095890411 1
 
< 0.1%
ValueCountFrequency (%)
60 77
2.8%
59.78356164 1
 
< 0.1%
59.68767123 1
 
< 0.1%
59.55342466 1
 
< 0.1%
59.52876712 1
 
< 0.1%
59.52328767 1
 
< 0.1%
59.26027397 1
 
< 0.1%
59.24657534 1
 
< 0.1%
58.82465753 1
 
< 0.1%
58.8 2
 
0.1%

job_stability_missing
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
0
2036 
1
711 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2747
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2036
74.1%
1 711
 
25.9%

Length

2025-05-17T17:11:56.161663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:56.327544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2036
74.1%
1 711
 
25.9%

Most occurring characters

ValueCountFrequency (%)
0 2036
74.1%
1 711
 
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2036
74.1%
1 711
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2036
74.1%
1 711
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2036
74.1%
1 711
 
25.9%

address_stability_missing
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.6 KiB
0
2686 
1
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2747
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2686
97.8%
1 61
 
2.2%

Length

2025-05-17T17:11:56.517669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:56.698667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2686
97.8%
1 61
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 2686
97.8%
1 61
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2686
97.8%
1 61
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2686
97.8%
1 61
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2686
97.8%
1 61
 
2.2%

cust_income_log
Real number (ℝ)

High correlation  Zeros 

Distinct2370
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6165543
Minimum0
Maximum8.9846889
Zeros76
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size21.6 KiB
2025-05-17T17:11:56.937665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.0953072
Q15.3735397
median5.6812222
Q36.0195743
95-th percentile6.7099522
Maximum8.9846889
Range8.9846889
Interquartile range (IQR)0.64603457

Descriptive statistics

Standard deviation1.076741
Coefficient of variation (CV)0.19170847
Kurtosis18.028581
Mean5.6165543
Median Absolute Deviation (MAD)0.32011785
Skewness-3.7462221
Sum15428.675
Variance1.1593712
MonotonicityNot monotonic
2025-05-17T17:11:57.275668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
2.8%
5.732345013 25
 
0.9%
5.445742182 24
 
0.9%
5.599276295 24
 
0.9%
5.95484046 22
 
0.8%
5.26428318 21
 
0.8%
5.377057451 12
 
0.4%
5.627344374 11
 
0.4%
5.223676708 11
 
0.4%
5.849768042 10
 
0.4%
Other values (2360) 2511
91.4%
ValueCountFrequency (%)
0 76
2.8%
0.7672551528 1
 
< 0.1%
4.773938777 1
 
< 0.1%
4.821087692 1
 
< 0.1%
4.873687082 1
 
< 0.1%
4.891909506 1
 
< 0.1%
4.930193062 1
 
< 0.1%
4.939096878 1
 
< 0.1%
4.948616399 1
 
< 0.1%
4.95307843 1
 
< 0.1%
ValueCountFrequency (%)
8.984688871 1
< 0.1%
8.83432465 1
< 0.1%
8.024529836 1
< 0.1%
8.00903695 1
< 0.1%
7.905776288 1
< 0.1%
7.901452793 1
< 0.1%
7.85977543 1
< 0.1%
7.858952698 1
< 0.1%
7.833639843 1
< 0.1%
7.760987551 1
< 0.1%

Interactions

2025-05-17T17:11:43.484579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:27.552644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:30.489170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:32.479171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:34.637171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:36.850171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:39.174582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:41.288579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:43.743579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:28.250171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:30.741170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:32.746171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:34.921170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:37.199580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:39.412580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:41.589579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:43.982580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:28.751169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:30.975169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:33.012170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:35.195170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:37.488581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:39.679581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:41.871578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:44.241655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:29.079170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:31.226169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:33.277170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:35.486169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:37.776578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:39.998581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:42.150580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:44.514657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:29.391171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:31.481171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:33.554170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:35.757172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:38.083579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:40.293580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:42.447579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:44.786658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:29.705169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:31.762170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:33.854170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:36.047169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:38.364582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:40.549578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:42.730582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:45.023654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:29.977171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:32.003169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:34.123171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:36.320170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:38.625580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:40.781581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:42.967578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:45.281657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:30.239170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:32.250171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:34.386171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:36.603170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:38.905581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:41.048580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:43.228583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-17T17:11:57.523666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
address_stability_missingaddress_stability_yearsagecocunutcurrent_balance_eurcust_incomecust_income_logeducationemploymentgenderjob_stability_missingjob_stability_yearsmarital_statusyears_with_bank
address_stability_missing1.0000.3920.0780.1370.0000.0000.6770.0260.5350.0000.1840.1200.0500.071
address_stability_years0.3921.0000.2600.017-0.036-0.109-0.1090.0550.2140.1840.2150.1470.0910.008
age0.0780.2601.000-0.0110.007-0.086-0.0860.0630.4980.1210.7150.4060.3340.257
cocunut0.1370.017-0.0111.0000.044-0.026-0.0260.0150.0400.0000.000-0.0010.0240.008
current_balance_eur0.000-0.0360.0070.0441.0000.1390.1390.1230.0970.0690.139-0.0050.0230.157
cust_income0.000-0.109-0.086-0.0260.1391.0001.0000.1170.0220.0500.0000.1830.0500.277
cust_income_log0.677-0.109-0.086-0.0260.1391.0001.0000.1780.2830.0840.2050.1830.0200.277
education0.0260.0550.0630.0150.1230.1170.1781.0000.1120.1230.0960.0610.0190.126
employment0.5350.2140.4980.0400.0970.0220.2830.1121.0000.1150.8510.4530.2290.121
gender0.0000.1840.1210.0000.0690.0500.0840.1230.1151.0000.0450.0880.2300.062
job_stability_missing0.1840.2150.7150.0000.1390.0000.2050.0960.8510.0451.0000.7290.2960.081
job_stability_years0.1200.1470.406-0.001-0.0050.1830.1830.0610.4530.0880.7291.0000.1970.315
marital_status0.0500.0910.3340.0240.0230.0500.0200.0190.2290.2300.2960.1971.0000.088
years_with_bank0.0710.0080.2570.0080.1570.2770.2770.1260.1210.0620.0810.3150.0881.000

Missing values

2025-05-17T17:11:45.753654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-17T17:11:46.186654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cocunutageyears_with_bankmarital_statuseducationemploymentgendercust_incomecurrent_with_bank_datecurrent_balance_eurjob_stability_yearsaddress_stability_yearsjob_stability_missingaddress_stability_missingcust_income_log
080001323MHGHPVEM423.0769232014-07-02143.00000011.76712331.726027006.049915
1800025110WOTHRETF140.6102312007-02-212288.7001548.7315075.956164104.953078
280003367MBCRSTEF326.9230772009-10-262268.4916927.07397334.994521005.792779
3800044611DBCRMISCF738.8200002005-11-304536.98346210.9342478.098630006.606407
4800053910MHGHPVEM483.9282312006-12-053076.9230773.34794523.109589006.184001
580006649MBCRRETF274.5153852008-06-16153.76923123.10411033.369863005.618643
680007648WHGHRETF237.6340002009-06-102307.6923088.73150738.038356105.474931
7800087411MHGHRETM221.0430772006-05-26790.3846158.73150719.972603105.402871
880009264SHGHPVEM248.6461542012-11-294615.3846154.7013707.013699005.520045
9800104213MBCRPVEF228.0769232004-06-294820.3214623.88767141.131507005.434058
cocunutageyears_with_bankmarital_statuseducationemploymentgendercust_incomecurrent_with_bank_datecurrent_balance_eurjob_stability_yearsaddress_stability_yearsjob_stability_missingaddress_stability_missingcust_income_log
273782738304MHGHPVEF215.3846152013-06-252307.6923084.6000002.432877005.377057
273882739678MHGHRETF253.2348462009-01-274742.3076928.73150735.536986105.538258
2739827405112MBCRSTEF553.8461542005-06-09300.68076919.35890449.912329006.318691
274082741658MHGHRETM202.1150772009-05-06790.3846158.73150740.616438105.313773
2741827423614SHGHPVEF395.2090002003-03-262307.6923089.33972611.271233005.981942
274282743535MOTHPVEF183.4615382012-05-045407.52430810.26575336.320548005.217441
274382744568MHGHSTEF276.9230772009-02-27253.4592319.4794529.479452005.627344
274482745675DBCRRETF148.7211542012-05-282371.15384619.1534254.095890005.008775
274582746481MHGHSTEF258.5615382015-12-152307.69230817.77534228.780822005.558994
2746827474414MHGHPVEM682.9541542002-12-207692.30769210.76712342.898630006.527891